Jaromír Běhal

Super-resolution imaging has revolutionized the study of systems ranging from molecular structures to distant galaxies. However, existing super-resolution methods require extensive calibration and retraining for each imaging setup, limiting their practical deployment. We introduce a device-agnostic deep-learning framework for super-resolution imaging of point-like emitters that eliminates the need for calibration data or explicit knowledge of optical system parameters. Our device-agnostic modeling utilizes diverse, numerically simulated dataset encompassing a broad range of imaging conditions, enabling generalization across different optical setups. Once trained, the model reconstructs super-resolved images directly from a single resolution-limited camera frame with superior accuracy and computational efficiency compared to state-of-the-art methods. We experimentally validate our approach using a custom microscopy setup with controllable ground-truth emitter positions. We also demonstrate its versatility on stellar astronomy and single-molecule localization microscopy datasets of point-like sources, achieving high resolution without prior information. Our findings establish a pathway toward universal, calibration-free super-resolution imaging, expanding its applicability across scientific disciplines.

Reliable identification of microplastic fibers is crucial for environmental monitoring but remains analytically challenging. We report the first explainable deep-learning framework for classifying microplastic and natural microfibers using exclusively polarization-based features obtained from polarization-resolved digital holographic microscopy. From multiplexed holograms, the complex Jones matrix of each fiber was reconstructed to extract polarization eigen-parameters describing optical anisotropy. Statistical descriptors of nine polarization characteristics formed a 72-dimensional feature vector for a total of 296 fibers spanning six material classes, including polyamide 6, polyethylene terephthalate, polyamide 6.6, polypropylene, cotton, and wool. The designed fully-connected deep neural network achieved an accuracy of 96.7 % on the validation data, surpassing that of common machine-learning classifiers. Explainable artificial intelligence analysis with Shapley additive explanations identified eigenvalue-ratio quantities as dominant predictors, revealing the physical basis for classification. An additional reduced-feature model with the preserved architecture, exploiting only these most significant eigenvalue-based characteristics, retained high accuracy (93.3 %), thereby confirming their dominant role while still outperforming common machine-learning classifiers. These results establish polarization-based features as distinctive optical fingerprints and demonstrate an explainable deep-learning approach for automated microplastic fiber identification.

Revealing the structural and functional organization of biological samples at submicrometer scales requires high-resolution imaging techniques that provide real-time, quantitative information. However, resolving and analyzing unlabeled biological structures at the subcellular level from a single-shot image remain challenging. We introduce a novel incoherent quantitative phase imaging technique based on lateral shearing digital holographic microscopy, with shearing distances exceeding the coherence area of the employed illumination—a regime not previously achieved and exploited. This strategy consequently ensures artifact-free, high-accuracy, and high-resolution quantitative phase reconstructions. This regime extends single-shot lateral shearing digital holographic microscopy toward incoherent illumination, thus increasing the space–time bandwidth product of the method. The practical common-path geometry also provides enhanced vibration resistance, which is essential for consistent time-lapse measurements. We verify the scalability and effectiveness of the method by investigating samples via multiple condenser–objective pairs, providing a wide range of lateral resolutions and fields of view, thereby ensuring its applicability for various microscope settings. We experimentally verify long-term phase stability and unprecedented phase-reconstruction accuracy. Finally, we use our method to investigate biological samples, including cheek cells, diatoms, and yeast cells, highlighting its potential for dynamic label-free analysis at the organelle level. These results establish our approach as a powerful tool for quantitative phase imaging, enabling high-precision investigations across a wide range of scientific applications.

The ability to resolve and quantify features at submicrometer scales from a single-shot image is crucial for real-time uncovering intricate structures in unlabeled biological samples and analyzing them at the subcellular level. We introduce a novel incoherent quantitative phase imaging technique based on lateral shearing digital holographic microscopy, with shearing distances exceeding the coherence area of the employed illumination -- a regime not previously achieved and exploited. This strategy consequently assures artifacts-free, high-accuracy, and high-resolution quantitative phase reconstructions. This regime extends the single-shot lateral shearing digital holographic microscopy towards incoherent illumination, thus increasing the space-time bandwidth product of the method. The practical common-path geometry also provides enhanced vibration resistance, which is essential for consistent time-lapse measurements. We verify the scalability and effectiveness of the method by investigating samples via multiple condenser-objective pairs, providing a wide range of lateral resolutions and fields of view, thereby assuring its applicability for various microscope settings. We experimentally verify long-term phase stability and unprecedented phase-reconstruction accuracy. Finally, we use our method for investigation of biological samples, including cheek cells, diatoms, and yeast cells, highlighting its potential for dynamic label-free analysis at the organelle level.

This study presents an innovative methodology to analyze a blood sample from a healthy donor, providing a quantitative characterization of white blood cells (WBCs). It aims to evaluate the effectiveness of holographic quantitative phase imaging (QPI) flow cytometry (FC) in examining phase-contrast maps at the cellular level, thereby enabling the identification and classification of granulocyte types. Additionally, we demonstrate that an unsupervised method can differentiate granulocyte sub-types, i.e., neutrophils and eosinophils. The results instill strong confidence in the potential future use of QPI FC for liquid biopsies and/or for assessing the heterogeneity of WBCs and, more broadly, to facilitate label-free blood tests.

Lithium Niobate is a ferroelectric material with interesting physical properties. In particular, Periodically Poled Lithium Niobate (PPLN) crystals have been used in diverse applications, such as non-linear optics or microlens array fabrication. In this work, we used a PPLN crystal having hexagonal reversed polarization domains, disposed on a square array of 200 μm period. We applied a temperature gradient to the PPLN and simultaneously observed it with a lensless incoherent holographic microscope. We observed that the phase of the inverse polarization domains varied depending on the temperature applied. Therefore, we induced a thermo-optical modulation of the PPLN crystal. We further analysed the behaviour of the PPLN, propagating the complex field beyond the crystal and plotting its intensity. We found that an elongated bright spot was formed at the centre of each hexagonal reversed polarization domain, due to diffraction. Given their shape and the nature of the phenomenon, these intensity spots are similar to Poisson spots. The intensity of the spots depended on the phase of the PPLN (hence, on the temperature applied). Therefore, we were able to generate a tunable Poisson spot array by controlling the temperature of the PPLN.

Accumulation of bioavailable heavy metals in aquatic environment poses a serious threat to marine communities and human health due to possible trophic transfers through the food chain of toxic, non-degradable, exogenous pollutants. Copper (Cu) is one of the most spread heavy metals in water, and can severely affect primary producers at high doses. Here we show a novel imaging test to assay the dose-dependent effects of Cu on live microalgae identifying stress conditions when they are still capable of sustaining a positive growth. The method relies on Fourier Ptychographic Microscopy (FPM), capable to image large field of view in label-free phase-contrast mode attaining submicron lateral resolution. We uniquely combine FPM with a new multi-scale analysis method based on fractal geometry. The system is able to provide ensemble measurements of thousands of diatoms in the liquid sample simultaneously, while ensuring at same time single-cell imaging and analysis for each diatom. Through new image descriptors, we demonstrate that fractal analysis is suitable for handling the complexity and informative power of such multiscale FPM modality. We successfully tested this new approach by measuring how different concentrations of Cu impact on Skeletonema pseudocostatum diatom populations isolated from the Sarno River mouth.

Inkjet printing of liquid crystal (LC) microlens arrays is particularly appealing for the development of switchable 2D/3D organic light-emitting diode (OLED) displays, as the printing process ensures that the lenses can be deposited directly and on-demand onto the pixelated OLED layer without the need for additional steps, thus simplifying fabrication complexity. Even if different fabrication technologies have been employed and good results in LC direct printing have already been achieved, all the systems used require costly equipment and heated nozzles to reduce the LC solution's viscosity. Here, we present the direct printing of a nematic LC (NLC) lens by a Drop-on-Demand (DoD) inkjet printing by a pyro-electrohydrodynamic effect for the first time. The method works at ambient temperature and avoids dispensing nozzles, thus offering a noncontact manipulation approach of liquid with high resolution and good repeatability on different kinds of substrates. NLC microlenses are printed on different substrates and fully characterized. Polarization properties are evaluated for various samples, i.e., NLC lenses on unaligned and indium-tin oxide (ITO) aligned. Moreover, an in-depth characterization of the NLC lenses is reported by polarized optical microscopy and by analyzing the birefringence in digital holographic microscopy.

Probiotic bacteria are widely used in pharmaceutics to offer health benefits. Microencapsulation is used to deliver probiotics into the human body. Capsules in the stomach have to keep bacteria constrained until release occurs in the intestine. Once outside, bacteria must maintain enough motility to reach the intestine walls. Here, we develop a platform based on two label-free optical modules for rapidly screening and ranking probiotic candidates in the laboratory. Bio-speckle dynamics assay tests the microencapsulation effectiveness by simulating the gastrointestinal transit. Then, a digital holographic microscope 3D-tracks their motility profiles at a single element level to rank the strains.